In image segmentation, a mask refers to a binary image where specific pixels are labeled to represent areas of interest or different regions within the image. Typically, these regions are classified as either foreground (objects of interest) or background. A mask is a crucial tool used in the process of segmenting an image into meaningful parts. For example, in semantic segmentation, where the goal is to label each pixel in an image with a corresponding class, the mask would contain a value of 1 for pixels belonging to an object class (e.g., a car or tree) and 0 for the background. Masks are used in various applications, such as object detection, medical imaging, or autonomous driving. In instance segmentation, a mask is even more specific and defines the exact boundaries of each distinct object instance in an image. The process of generating a mask involves using algorithms that differentiate various objects or regions in an image based on features like color, texture, and intensity.
What is a mask in image segmentation?
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Vector Database 101: Everything You Need to Know
- Embedding 101
- AI & Machine Learning
- GenAI Ecosystem
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What is the parameter count of DeepSeek's R1 model?
The parameter count of DeepSeek's R1 model is 1.3 billion. This number indicates the total number of adjustable paramete
How does vector normalization affect embeddings?
Vector normalization is a process that scales a vector so that its length, or magnitude, becomes equal to one. This is p
How do benchmarks assess data governance compliance?
Benchmarks assess data governance compliance by providing clear standards and metrics against which an organization can